





"""simple docstring"""





import argparse
import os
from pathlib import Path

import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download

from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
    BarkCoarseConfig,
    BarkConfig,
    BarkFineConfig,
    BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
    BarkCoarseGenerationConfig,
    BarkFineGenerationConfig,
    BarkGenerationConfig,
    BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__   =  logging.get_logger(__name__)

set_seed(770)


SCREAMING_SNAKE_CASE__   =  {
    "c_attn": "att_proj",
    "c_proj": "out_proj",
    "c_fc": "in_proj",
    "transformer.": "",
    "h.": "layers.",
    "ln_1": "layernorm_1",
    "ln_2": "layernorm_2",
    "ln_f": "layernorm_final",
    "wpe": "position_embeds_layer",
    "wte": "input_embeds_layer",
}


SCREAMING_SNAKE_CASE__   =  {
    "text_small": {
        "repo_id": "suno/bark",
        "file_name": "text.pt",
    },
    "coarse_small": {
        "repo_id": "suno/bark",
        "file_name": "coarse.pt",
    },
    "fine_small": {
        "repo_id": "suno/bark",
        "file_name": "fine.pt",
    },
    "text": {
        "repo_id": "suno/bark",
        "file_name": "text_2.pt",
    },
    "coarse": {
        "repo_id": "suno/bark",
        "file_name": "coarse_2.pt",
    },
    "fine": {
        "repo_id": "suno/bark",
        "file_name": "fine_2.pt",
    },
}

SCREAMING_SNAKE_CASE__   =  os.path.dirname(os.path.abspath(__file__))
SCREAMING_SNAKE_CASE__   =  os.path.join(os.path.expanduser("~"), ".cache")
SCREAMING_SNAKE_CASE__   =  os.path.join(os.getenv("XDG_CACHE_HOME", default_cache_dir), "suno", "bark_v0")


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Dict				,		SCREAMING_SNAKE_CASE	:		List[str]=False     ):







      '''simple docstring'''




      lowerCAmelCase    		=			model_type
      if use_small:
            key += "_small"
      return os.path.join(SCREAMING_SNAKE_CASE				,		REMOTE_MODEL_PATHS[key]["""file_name"""]     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Any				,		SCREAMING_SNAKE_CASE	:		str     ):







      '''simple docstring'''




      os.makedirs(SCREAMING_SNAKE_CASE				,		exist_ok=SCREAMING_SNAKE_CASE     )
      hf_hub_download(repo_id=SCREAMING_SNAKE_CASE				,		filename=SCREAMING_SNAKE_CASE				,		local_dir=SCREAMING_SNAKE_CASE     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		Optional[Any]				,		SCREAMING_SNAKE_CASE	:		Optional[int]=False				,		SCREAMING_SNAKE_CASE	:		Tuple="text"     ):







      '''simple docstring'''




      if model_type == "text":
            lowerCAmelCase    		=			BarkSemanticModel
            lowerCAmelCase    		=			BarkSemanticConfig
            lowerCAmelCase    		=			BarkSemanticGenerationConfig
      elif model_type == "coarse":
            lowerCAmelCase    		=			BarkCoarseModel
            lowerCAmelCase    		=			BarkCoarseConfig
            lowerCAmelCase    		=			BarkCoarseGenerationConfig
      elif model_type == "fine":
            lowerCAmelCase    		=			BarkFineModel
            lowerCAmelCase    		=			BarkFineConfig
            lowerCAmelCase    		=			BarkFineGenerationConfig
      else:
            raise NotImplementedError()
      lowerCAmelCase    		=			F'{model_type}_small' if use_small else model_type
      lowerCAmelCase    		=			REMOTE_MODEL_PATHS[model_key]
      if not os.path.exists(SCREAMING_SNAKE_CASE     ):
            logger.info(F'{model_type} model not found, downloading into `{CACHE_DIR}`.'     )
            _download(model_info["""repo_id"""]				,		model_info["""file_name"""]     )
      lowerCAmelCase    		=			torch.load(SCREAMING_SNAKE_CASE				,		map_location=SCREAMING_SNAKE_CASE     )
      # this is a hack
      lowerCAmelCase    		=			checkpoint["""model_args"""]
      if "input_vocab_size" not in model_args:
            lowerCAmelCase    		=			model_args["""vocab_size"""]
            lowerCAmelCase    		=			model_args["""vocab_size"""]
            del model_args["vocab_size"]

      # convert Bark model arguments to HF Bark model arguments
      lowerCAmelCase    		=			model_args.pop("""n_head"""     )
      lowerCAmelCase    		=			model_args.pop("""n_embd"""     )
      lowerCAmelCase    		=			model_args.pop("""n_layer"""     )

      lowerCAmelCase    		=			ConfigClass(**checkpoint["""model_args"""]     )
      lowerCAmelCase    		=			ModelClass(config=SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			GenerationConfigClass()

      lowerCAmelCase    		=			model_generation_config
      lowerCAmelCase    		=			checkpoint["""model"""]
      # fixup checkpoint
      lowerCAmelCase    		=			"""_orig_mod."""
      for k, v in list(state_dict.items()     ):
            if k.startswith(SCREAMING_SNAKE_CASE     ):
                  # replace part of the key with corresponding layer name in HF implementation
                  lowerCAmelCase    		=			k[len(SCREAMING_SNAKE_CASE     ) :]
                  for old_layer_name in new_layer_name_dict:
                        lowerCAmelCase    		=			new_k.replace(SCREAMING_SNAKE_CASE				,		new_layer_name_dict[old_layer_name]     )

                  lowerCAmelCase    		=			state_dict.pop(SCREAMING_SNAKE_CASE     )

      lowerCAmelCase    		=			set(state_dict.keys()     ) - set(model.state_dict().keys()     )
      lowerCAmelCase    		=			{k for k in extra_keys if not k.endswith(""".attn.bias"""     )}
      lowerCAmelCase    		=			set(model.state_dict().keys()     ) - set(state_dict.keys()     )
      lowerCAmelCase    		=			{k for k in missing_keys if not k.endswith(""".attn.bias"""     )}
      if len(SCREAMING_SNAKE_CASE     ) != 0:
            raise ValueError(F'extra keys found: {extra_keys}'     )
      if len(SCREAMING_SNAKE_CASE     ) != 0:
            raise ValueError(F'missing keys: {missing_keys}'     )
      model.load_state_dict(SCREAMING_SNAKE_CASE				,		strict=SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			model.num_parameters(exclude_embeddings=SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			checkpoint["""best_val_loss"""].item()
      logger.info(F'model loaded: {round(n_params/1e6				,		1     )}M params, {round(SCREAMING_SNAKE_CASE				,		3     )} loss'     )
      model.eval()
      model.to(SCREAMING_SNAKE_CASE     )
      del checkpoint, state_dict

      return model


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Optional[Any]				,		SCREAMING_SNAKE_CASE	:		Any=False				,		SCREAMING_SNAKE_CASE	:		List[Any]="text"     ):







      '''simple docstring'''




      if model_type not in ("text", "coarse", "fine"):
            raise NotImplementedError()

      lowerCAmelCase    		=			"""cpu"""  # do conversion on cpu

      lowerCAmelCase    		=			_get_ckpt_path(SCREAMING_SNAKE_CASE				,		use_small=SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			_load_model(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		model_type=SCREAMING_SNAKE_CASE				,		use_small=SCREAMING_SNAKE_CASE     )

      # load bark initial model
      lowerCAmelCase    		=			_bark_load_model(SCREAMING_SNAKE_CASE				,		"""cpu"""				,		model_type=SCREAMING_SNAKE_CASE				,		use_small=SCREAMING_SNAKE_CASE     )

      if model_type == "text":
            lowerCAmelCase    		=			bark_model["""model"""]

      if model.num_parameters(exclude_embeddings=SCREAMING_SNAKE_CASE     ) != bark_model.get_num_params():
            raise ValueError("""initial and new models don't have the same number of parameters"""     )

      # check if same output as the bark model
      lowerCAmelCase    		=			5
      lowerCAmelCase    		=			10

      if model_type in ["text", "coarse"]:
            lowerCAmelCase    		=			torch.randint(2_56				,		(batch_size, sequence_length)				,		dtype=torch.int     )
            lowerCAmelCase    		=			bark_model(SCREAMING_SNAKE_CASE     )[0]

            lowerCAmelCase    		=			model(SCREAMING_SNAKE_CASE     )

            # take last logits
            lowerCAmelCase    		=			output_new_model_total.logits[:, [-1], :]

      else:
            lowerCAmelCase    		=			3
            lowerCAmelCase    		=			8
            lowerCAmelCase    		=			torch.randint(2_56				,		(batch_size, sequence_length, n_codes_total)				,		dtype=torch.int     )

            lowerCAmelCase    		=			model(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )
            lowerCAmelCase    		=			bark_model(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )

            lowerCAmelCase    		=			output_new_model_total.logits

      # output difference should come from the difference of self-attention implementation design
      if output_new_model.shape != output_old_model.shape:
            raise ValueError("""initial and new outputs don't have the same shape"""     )
      if (output_new_model - output_old_model).abs().max().item() > 1e-3:
            raise ValueError("""initial and new outputs are not equal"""     )

      Path(SCREAMING_SNAKE_CASE     ).mkdir(exist_ok=SCREAMING_SNAKE_CASE     )
      model.save_pretrained(SCREAMING_SNAKE_CASE     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		List[Any]				,		SCREAMING_SNAKE_CASE	:		Optional[int]				,		SCREAMING_SNAKE_CASE	:		Optional[int]				,		SCREAMING_SNAKE_CASE	:		Optional[Any]				,		SCREAMING_SNAKE_CASE	:		Union[str, Any]				,		SCREAMING_SNAKE_CASE	:		Union[str, Any]				,		):







      '''simple docstring'''




      lowerCAmelCase    		=			os.path.join(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )

      lowerCAmelCase    		=			BarkSemanticConfig.from_pretrained(os.path.join(SCREAMING_SNAKE_CASE				,		"""config.json"""     )     )
      lowerCAmelCase    		=			BarkCoarseConfig.from_pretrained(os.path.join(SCREAMING_SNAKE_CASE				,		"""config.json"""     )     )
      lowerCAmelCase    		=			BarkFineConfig.from_pretrained(os.path.join(SCREAMING_SNAKE_CASE				,		"""config.json"""     )     )
      lowerCAmelCase    		=			EncodecConfig.from_pretrained("""facebook/encodec_24khz"""     )

      lowerCAmelCase    		=			BarkSemanticModel.from_pretrained(SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			BarkCoarseModel.from_pretrained(SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			BarkFineModel.from_pretrained(SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			EncodecModel.from_pretrained("""facebook/encodec_24khz"""     )

      lowerCAmelCase    		=			BarkConfig.from_sub_model_configs(
          SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )

      lowerCAmelCase    		=			BarkGenerationConfig.from_sub_model_configs(
          semantic.generation_config				,		coarseAcoustic.generation_config				,		fineAcoustic.generation_config     )

      lowerCAmelCase    		=			BarkModel(SCREAMING_SNAKE_CASE     )

      lowerCAmelCase    		=			semantic
      lowerCAmelCase    		=			coarseAcoustic
      lowerCAmelCase    		=			fineAcoustic
      lowerCAmelCase    		=			codec

      lowerCAmelCase    		=			bark_generation_config

      Path(SCREAMING_SNAKE_CASE     ).mkdir(exist_ok=SCREAMING_SNAKE_CASE     )
      bark.save_pretrained(SCREAMING_SNAKE_CASE				,		repo_id=SCREAMING_SNAKE_CASE				,		push_to_hub=SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__   =  argparse.ArgumentParser()
       # Required parameters

       parser.add_argument("model_type", type=str, help="text, coarse or fine.")
       parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
       parser.add_argument("--is_small", action="store_true", help="convert the small version instead of the large.")

       SCREAMING_SNAKE_CASE__   =  parser.parse_args()

       load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)













"""simple docstring"""





import logging
import os

from .state import PartialState

class 						lowercase   (   logging.LoggerAdapter   ):





      @staticmethod
      def        _snake_case     (			lowercase					)						->	Optional[Any]:
            lowerCAmelCase    		=			PartialState()
            return not main_process_only or (main_process_only and state.is_main_process)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		*lowercase ,		**lowercase					)						->	Dict:
            if PartialState._shared_state == {}:
                  raise RuntimeError(
                      """You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility."""					)
            lowerCAmelCase    		=			kwargs.pop("""main_process_only""" ,		lowercase					)
            lowerCAmelCase    		=			kwargs.pop("""in_order""" ,		lowercase					)

            if self.isEnabledFor(lowercase					):
                  if self._should_log(lowercase					):
                        lowerCAmelCase		,						lowerCAmelCase    		=			self.process(lowercase ,		lowercase					)
                        self.logger.log(lowercase ,		lowercase ,		*lowercase ,		**lowercase					)

                  elif in_order:
                        lowerCAmelCase    		=			PartialState()
                        for i in range(state.num_processes					):
                              if i == state.process_index:
                                    lowerCAmelCase		,						lowerCAmelCase    		=			self.process(lowercase ,		lowercase					)
                                    self.logger.log(lowercase ,		lowercase ,		*lowercase ,		**lowercase					)
                              state.wait_for_everyone()







def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		str = None     ):







      '''simple docstring'''




      if log_level is None:
            lowerCAmelCase    		=			os.environ.get("""ACCELERATE_LOG_LEVEL"""				,		SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			logging.getLogger(SCREAMING_SNAKE_CASE     )
      if log_level is not None:
            logger.setLevel(log_level.upper()     )
            logger.root.setLevel(log_level.upper()     )
      return MultiProcessAdapter(SCREAMING_SNAKE_CASE				,		{}     )






